@Article{Kazemi:2016:PT,
author = "Pezhman Kazemi and Mohammad Hassan Khalid and
Jakub Szlek and Andreja Mirtic and Gavin K. Reynolds and
Renata Jachowicz and Aleksander Mendyk",
title = "Computational intelligence modeling of granule size
distribution for oscillating milling",
journal = "Powder Technology",
volume = "301",
pages = "1252--1258",
year = "2016",
ISSN = "0032-5910",
DOI = "doi:10.1016/j.powtec.2016.07.046",
URL = "http://www.sciencedirect.com/science/article/pii/S0032591016304387",
abstract = "Oscillating mills such as OscilloWitta (Frewitt) have
been widely used in the secondary manufacture of solid
dosage forms in the pharmaceutical industry. This type
of mill is generally used for moderate milling of
difficult-to-process and heat sensitive materials to a
particle size range of c.a. 250 micrometers. Particle
size distribution is the result of interaction between
ribbon properties and process conditions, therefore it
is crucial to model and optimize such a complex process
in order to produce more uniform particle size
distributions. In this work, multiple linear regression
(MLR), genetic programming (GP), and artificial Neural
Networks (ANN) assisted by 3-fold cross-validation (CV)
were used to present generalized models for the
prediction of granule size based on the experimental
data set. The normalized mean squared error (NRMSE) and
the coefficient of determination (R2) for best fit,
namely ANN model were obtained as follows: NRMSE =
2.28percent, R2 = 0.9926. MLR model was imprecise in
the prediction of d10 class. Due to its performance
similarities to ANN and its transparency and ease of
application, the GP model could be used widely for
granule size prediction. Based on the results it was
confirmed that the screen size has the most significant
effect on the granule size distribution.",
keywords = "genetic algorithms, genetic programming, Oscillating
milling, Neural network, Roll compaction, Dry
granulation, Modeling",
}